Probability Theory: Discrete Random Variables

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Explores random variables, expectation, and independence in probability theory.

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Explores Bayes' Theorem for defective parts detection, discrete random variables, and distribution functions, with practical examples and exercises.

Covers the normal distribution, including its properties and calculations.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Introduces random variables, probability distributions, and expected values through practical examples.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Covers continuous random variables, probability density functions, and distributions, with practical examples.